Avoid Filling Swiss Cheese with Whipped Cream: Imputation Techniques and Evaluation Procedures for Cross-Country Time Series

Michael Weber and Michaela Denk

No 2011/151, IMF Working Papers from International Monetary Fund

Abstract: International organizations collect data from national authorities to create multivariate cross-sectional time series for their analyses. As data from countries with not yet well-established statistical systems may be incomplete, the bridging of data gaps is a crucial challenge. This paper investigates data structures and missing data patterns in the cross-sectional time series framework, reviews missing value imputation techniques used for micro data in official statistics, and discusses their applicability to cross-sectional time series. It presents statistical methods and quality indicators that enable the (comparative) evaluation of imputation processes and completed datasets.

Keywords: WP; time series data; data pattern; imputed dataset; cross-sectional time series; data augmentation; data technique; expected value; information criteria; quality report; time series model (search for similar items in EconPapers)
Pages: 27
Date: 2011-06-01
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Citations: View citations in EconPapers (6)

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